DISENTANGLING THE ROLE OF HIGH SCHOOL GRADES, SAT ® SCORES, AND SES IN PREDICTING COLLEGE ACHIEVEMENT
Post on 10-Apr-2017
Disentangling the Role of High School Grades, SAT Scores, and SES in Predicting College Achievement
Research Report ETS RR13-09
ETS Research Report Series
EIGNOR EXECUTIVE EDITOR James Carlson
Beata Beigman Klebanov Research Scientist
Heather Buzick Research Scientist
Brent Bridgeman Distinguished Presidential Appointee
Keelan Evanini Research Scientist
Marna Golub-Smith Principal Psychometrician
Shelby Haberman Distinguished Presidential Appointee
Gary Ockey Research Scientist
Donald Powers Managing Principal Research Scientist
Frank Rijmen Principal Research Scientist
John Sabatini Managing Principal Research Scientist
Matthias von Davier Director, Research
Rebecca Zwick Distinguished Presidential Appointee
Kim Fryer Manager, Editing Services
Ruth Greenwood Editor
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Disentangling the Role of High School Grades, SAT Scores, and SES
in Predicting College Achievement
ETS, Princeton, New Jersey
Action Editor: Marna Golub-Smith
Reviewers: Brent Bridgeman and Sandip Sinharay
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Focusing on high school grade-point average (HSGPA) in college admissions may foster ethnic
diversity and communicate the importance of high school performance. It has further been
claimed that HSGPA is the best single predictor of college grades and that it is more equitable
than test scores because of a smaller association with socioeconomic status (SES). Recent
findings, however, suggest that HSPGAs seemingly smaller correlation with SES is a
methodological artifact. In addition, it tends to produce systematic errors in the prediction of
college grades. Although supplementing HSGPA with a high-school resource index can mitigate
these errors, determining whether to include such an index in admissions decisions must take
into account the institutional mission and the potential diversity impact.
Key words: college admissions, socioeconomic status, SAT, high school grades
I am grateful for the support of the College Board, which provided data for the Zwick and Green
(2007) and Zwick and Himelfarb (2011) studies and provided funding for the Zwick and
Himelfarb study. Both studies were conducted at the University of California, Santa Barbara.
This paper benefitted from comments by Brent Bridgeman, Marna Golub-Smith, and Sandip
Attempts to statistically forecast college grades using high school performance and
admissions test scores have a long history, dating back at least as far as 1939, when Theodore
Sarbin compared predictions of first-quarter college grades obtained via regression analysis to
predictions based on counselor judgment (Sarbin, 1943; the regression equation won, albeit by a
small amount.) Since that time, thousands of statistical studies have been conducted in which the
goal was to predict college grade-point average using test scores (nearly always the SAT or
ACT) and high school grade-point average (HSGPA).
These studies fall into two categories. First, many undergraduate institutions conduct
research to predict students college performance using various combinations of possible
admissions criteria (e.g., Geiser, 2004). For reasons of convenience, the most commonly used
measure of college performance is first-year grade-point average (FGPA). Second, testing
companies conduct similar studies to investigate the validity of admissions tests as predictors of
college achievement (e.g., Kobrin, Patterson, Shaw, Mattern, & Barbuti, 2008). These two kinds
of studies typically assess the predictive value of both HSGPA and admissions test scores
because these are the academic indicators most often used in the college admissions process.
The resulting body of research literature has produced one remarkably consistent belief:
High school grade-point average is the best predictor of college grade-point average. For
example, the introduction to the new book, SAT Wars, states, Lest there be any confusion about
this, one should keep in mind that high school grade-point average (HSGPA) has always been
the best single academic variable predicting college grades (Soares, 2012, p. 6). And in their
essay on college admissions testing, Atkinson and Geiser (2009, p. 666) asserted that it is useful
to begin any discussion of standardized admissions tests with acknowledgment that a students
record in college preparatory courses in high school remains the best indicator of how the student
is likely to perform in college.
In fact, the determination of whether high school grades are the optimal predictor of
college performance may depend on the evaluation criteria. One way to compare the quality of
predictors is in terms of how highly correlated they are with the target of predictionin this
case, college grades. (A similar comparison can be conducted in terms of the R2 values from a
regression analysis, as explained below.) From this perspective, high school GPA tends to make
a relatively strong showing. For example, in a widely publicized study of the SAT at the
University of California, Geiser (2004) found the squared correlation between HSGPA and
FGPA to be .15, compared to only .13 for SAT scores. Yet it can be misleading to evaluate a
predictor simply in terms of correlations. A second aspect of prediction quality that is often
overlooked involves systematic prediction errors that can exist for some student groups, even in
the presence of a strong association between the predictor and the predicted quantity. With
respect to this criterion, HSGPA does not perform well as a predictor. A third criterion that has
emerged may be more political than technical: Some commentators argue that predictors of
college performance should be unassociated with socioeconomic status (SES) to the degree
possible. From this perspective, HSGPA has been deemed a superior predictor to test scores
because it is said to be relatively untainted by SES and thus a more legitimate predictor of
FGPA. For example, Geiser and Santelices (2007) asserted that [c]ompared to high-school
grade-point average (HSGPA), scores on standardized admissions tests such as the SAT I [an
earlier name for the SAT] are much more closely correlated with students socioeconomic
background characteristics (p. 2) and use this claim to support their conclusion that high-
school grades provide a fairer, more equitable and ultimately more meaningful basis for
admissions decision-making (p. 27). Their discussion is in keeping with the belief, expressed
in some recent critiques of testing, that correlations between admissions test scores and
subsequent grades are largely, if not entirely, an artifact of the common influences of SES on
both test scores and grades (see Sackett, Kuncel, Arneson, Cooper, & Waters, 2009, p. 2 for a
discussion).1 Again, however, a more fine-grained analysis yields a different conclusion.
In the following section, the findings of several large studies that sought to predict
college grades using test scores and HSGPA are described. The discussion here is restricted to
the SAT, but is largely applicable to the ACT as well. The third section summarizes the results of
a study that illustrates the substantial prediction errors that result for some ethnic groups when
HSGPA alone is used as a predictor of college performance. These errors are mitigated by the
inclusion of an index intended to serve as a proxy for school resources and also by the inclusion
of SAT scores. The fourth section describes a study that calls into question the claims about the
degree to which SAT scores and HSGPA are associated with SES. The results suggest that test
scores and high school grades have similar correlations with SES at the student level. These
findings are pertinent to the role of HSGPA in predicting FGPA. However, as addressed in the
Discussion section, predicted college GPA need not be the basis for college admissions
decisions. In fact, predicted performance need not play any role at all. Admissions criteria must
be established so as to support the goals of the institution.
Role of High School GPA in Equations for Predicting College GPA
Studies that assess the strength of association between academic indicators (such as
standardized admissions test scores and HSGPA) and college success at undergraduate
institutions are typically conducted by performing a regression analysis in which SAT scores and
HSGPA are used to predict first-year FGPA for students who already have FGPAs on record.
(Whether FGPA is the optimal college performance measure is, of course, debatable. Some
studies have used college course grades, later college GPAs, or degree attainment as success
criteria. See, e.g., Burton & Ramist, 2001; Geiser & Santelices, 2007.) The correlation between
the predicted FGPA and the actual FGPA earned by the students can then be estimated. This
correlation (R), or more commonly, its square (R2), is used as a measure of the strength of
prediction. The strength of prediction can also be evaluated for equations that include HSGPA
only or admissions test scores only so that these competing models can be compared. The
squared correlation for each model can be interpreted as the proportion of variance in FGPA that
is attributable to, or explained by the predictors. These analyses not only produce results that are
useful in evaluating the various prediction models, but also yield equations that can be used to
predict the performance of applicants.
Table 1 gives the relevant squared correlations for three large studies that have appeared
in recent years. There are several noteworthy aspects to the results. First, the joint contribution of
HSGPA and SAT is less than the sum of their individual contributions because the predictors are
not independent of each other. Second, both the results for SAT only and the results for HSGPA
and SAT combined are nearly identical across the three studies, despite the differences among
the studies in terms of the samples of students, the time of data collection, and other
methodological factors. Twelve to 13% of the variance in FGPA was attributable to SAT scores;
21 to 22% of the variance was explained by HSGPA and SAT scores combined. The third
notable finding in the table is that the squared correlation for HSGPA only is always larger than
the squared correlation for SAT alone (although the disparity is large only in the Zwick and Sklar
(2005) study, which was based on data that are now roughly three decades old). This recurrent
finding is the basis for the claim that high school grades are the best predictor of college grades.
Squared Correlations (R2) Between Predictors and First-Year College GPA in Three Studies
Kobrin et al. (2008, p. 5)
Geiser (2004, p. 130)
Zwick & Sklar (2005, p. 451)
HSGPA only .13 .15 .21
SAT only .12 .13 .12
HSGPA plus SAT .21 .21 .22
Nature of sample Students in the fall 2006 entering cohort at 110 institutions around the United States
Students who entered seven campuses of the University of California in 19961999. (UC Riverside entrants for 1997 and 1998 were excluded because of data problems. UC Santa Cruz was excluded because it did not provide conventional grades during this period.)
Students in the 1992 follow-up of the High School and Beyond (HSB) 1980 sophomore cohort, a national probability sample
Number of students 151,316 77,893 4,617
Details on HSGPA HSGPA was self-reported on the SAT Questionnaire in terms of 12 ordered categories.
HS grades were from transcripts. HSGPA was the honors-weighted GPA used by UC at the time of the study: Additional grade points were included for honors courses.
HSGPA was from transcripts.
Details on SAT scores SAT Math, Critical Reading, and Writing scores (components of the SAT since 2005) were included separately in the regressions.
SAT score was the sum of SAT Math and Verbal scores.
In the HSB database, SAT score was the sum of Math and Verbal scores. For students who took only the ACT or PSAT/NMSQT, an SAT-equivalent score is substituted.
Note. No corrections have been applied to the R2 values. All studies excluded students with
missing data. GPA = grade-point average; HSGPA = high school grade-point average.
It is worth noting that there have been exceptions to this pattern of superior predictive
value for HSGPA. For example, in the most recent of the four University of California (UC)
entry cohorts studied by Geiser (2004, p. 130), SAT scores were found to be more predictive of
FGPA than was HSGPA. In a later study of two UC entering classes, Agronow and Studley
(2007) found that in 2006, but not 2004, the SAT showed a very small advantage over HSGPA
in terms of predictive strength. Kobrin et al. (2008) found the SAT to be slightly more predictive
than HSGPA in certain categories of institutions, including those with admission rates under
50%, and those with fewer than 2,000 undergraduates. But more significant than these
exceptions is the fact that, as detailed in the...